Auxiliary resource planning in a flexible flow shop scheduling problem considering stage skipping. (February 2022)

• Record Type: Journal Article
• Authors: Hasani, Ali; Hosseini, Seyed Mohammad Hassan
• Is Part Of: Computers & operations research. Volume 138(2022)
• Publication Date: 2022-02
• Subjects: Scheduling -- Flexible flow shop -- Auxiliary resource -- Unrelated parallel machines -- Machine eligibility; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2021.105625
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: Optimizing the number of auxiliary resources is investigated in FFSP. Machine-dependent process stages issue is introduced as a new realistic condition. Some novel optimization models are developed to formulate and solve the problem. New approaches are presented considering the budget constraints and cost aspects. Abstract: This paper investigates a flexible flow shop scheduling problem wherein there are some unrelated parallel machines with different technology levels in the first stage. Some of these machines are multifunctional that can do several processes on jobs. Therefore, the jobs assigned to these machines do not require processing in some of the next stages. Although the majority of the traditional scheduling problems deal with machines as the only resource, there are usually unavailability constraints to auxiliary resources such as labor, tools, pallets, industrial robots, and so on. This challenge with inspiration from a spring manufacturing plant is tackled in the considered problem with machine eligibility and sequence-dependent setup times. This problem is described with a numeric example, and its parameters and decision variables are defined. Then, to formulate the problem, a mixed-integer linear programming model is developed to minimize the makespan and determine the number of required auxiliary resources. Then the genetic algorithm is calibrated for all aforementioned features of the problem at hand as the main solution approach. Furthermore, two other well-known metaheuristics i.e., simulated annealing and enhanced particle swarm optimization are modified and employed for computational comparisons. The result shows that the proposed algorithm provides accurate results regarding the number of required auxiliary resources to achieve the minimum makespan. Moreover, supplementary analysis is presented considering practical constraints such as budget and desired efficiency of machines. The result indicates that the solution approach could provide proper alternatives for managers in various conditions.